基于距离量测的双领航多AUV协同定位队形优化分析  

Optimality analysis for formation of MAUV cooperative localization with two leaders based on range measurements

作　　者：马朋[1] 张福斌[1] 徐德民[1] 

机构地区：[1]西北工业大学航海学院,西安710072

出　　处：《控制与决策》2018年第2期256-262,共7页Control and Decision

基　　金：国家自然科学基金项目(61273333)

摘　　要：利用领航与跟随多自主水下航行器(AUV)间相对距离量测信息进行协同定位,可有效提高多AUV编队整体定位精度.针对多AUV编队队形对协同定位性能所造成的影响,利用Fisher信息矩阵(FIM)行列式的对数构建协同定位性能评价函数,并通过评价函数的最大化实现编队队形的优化.分析并验证多AUV编队含有两个跟随AUV及两个以上跟随AUV共圆情况下的最优队形,并利用梯度下降算法进行迭代搜索,从而获得两个以上跟随AUV不共圆情况下的优化队形.最后,通过仿真实验结果验证结论的正确性及队形优化算法的有效性.The cooperative localization technique using relative range measurement information between a leader and a follower AUV can significantly improve the overall localization accuracy of the multiple AUV team. For the influence of the multiple AUV formation geometry on the performance of cooperative localization, the logarithms of determinants of Fisher information matrices（FIM） are used to generate an evaluation criterion for the performance of cooperative localization, which can be maximized to derive the optimal formation geometry. When the multiple AUV team consists of two follower AUVs or more than two concyclic follower AUVs, the optimal formation geometry for cooperative localization is analyzed and verified. A gradient descent algorithm is designed to iteratively seek the optimization formation in the case of more than two non-concyclic follower AUVs. Simulation results show the effectiveness of analytical results and the practicality of the proposed formation optimization algorithm.

关 键 词：多自主水下航行器 协同定位 Fisher信息矩阵 梯度下降 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]